numpy多维数组的创建
多维数组(矩阵ndarray)
ndarray的基本属性
-
shape
维度的大小 -
ndim
维度的个数 -
dtype
数据类型
1.1 随机抽样创建
1.1.1 rand
生成指定维度的随机多维度浮点型数组,区间范围是[0,1)
1 2 3 4 5 6 7 8 9 | Random values in a given shape. Create an array of the given shape and populate it with random samples from a uniform distribution over ``[ 0 , 1 )``. nd1 = np.random.rand( 1 , 1 ) print (nd1) print ( '维度的个数' ,nd1.ndim) print ( '维度的大小' ,nd1.shape) print ( '数据类型' ,nd1.dtype) # float 64 |
1.1.2 uniform
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | def uniform(low = 0.0 , high = 1.0 , size = None ): # real signature unknown; restored from __doc__ """ uniform(low=0.0, high=1.0, size=None) Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval ``[low, high)`` (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by `uniform`. Parameters ---------- low : float or array_like of floats, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float or array_like of floats Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``low`` and ``high`` are both scalars. Otherwise, ``np.broadcast(low, high).size`` samples are drawn. Returns ------- out : ndarray or scalar Drawn samples from the parameterized uniform distribution. See Also -------- randint : Discrete uniform distribution, yielding integers. random_integers : Discrete uniform distribution over the closed interval ``[low, high]``. random_sample : Floats uniformly distributed over ``[0, 1)``. random : Alias for `random_sample`. rand : Convenience function that accepts dimensions as input, e.g., ``rand(2,2)`` would generate a 2-by-2 array of floats, uniformly distributed over ``[0, 1)``. Notes ----- The probability density function of the uniform distribution is .. math:: p(x) = frac{1}{b - a} anywhere within the interval ``[a, b)``, and zero elsewhere. When ``high`` == ``low``, values of ``low`` will be returned. If ``high`` >> s = np.random.uniform(-1,0,1000) All values are within the given interval: >>> np.all(s >= -1) True >>> np.all(s >> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show() """ pass |
1 2 3 4 5 | nd2 = np.random.uniform( - 1 , 5 ,size = ( 2 , 3 )) print (nd2) print ( '维度的个数' ,nd2.ndim) print ( '维度的大小' ,nd2.shape) print ( '数据类型' ,nd2.dtype) |
运行结果:
1.1.3 randint
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | def randint(low, high = None , size = None , dtype = 'l' ): # real signature unknown; restored from __doc__ """ randint(low, high=None, size=None, dtype='l') Return random integers from `low` (inclusive) to `high` (exclusive). Return random integers from the "discrete uniform" distribution of the specified dtype in the "half-open" interval [`low`, `high`). If `high` is None (the default), then results are from [0, `low`). Parameters ---------- low : int Lowest (signed) integer to be drawn from the distribution (unless ``high=None``, in which case this parameter is one above the *highest* such integer). high : int, optional If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if ``high=None``). size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. dtype : dtype, optional Desired dtype of the result. All dtypes are determined by their name, i.e., 'int64', 'int', etc, so byteorder is not available and a specific precision may have different C types depending on the platform. The default value is 'np.int'. .. versionadded:: 1.11.0 Returns ------- out : int or ndarray of ints `size`-shaped array of random integers from the appropriate distribution, or a single such random int if `size` not provided. See Also -------- random.random_integers : similar to `randint`, only for the closed interval [`low`, `high`], and 1 is the lowest value if `high` is omitted. In particular, this other one is the one to use to generate uniformly distributed discrete non-integers. Examples -------- >>> np.random.randint(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) >>> np.random.randint(1, size=10) array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) Generate a 2 x 4 array of ints between 0 and 4, inclusive: >>> np.random.randint(5, size=(2, 4)) array([[4, 0, 2, 1], [3, 2, 2, 0]]) """ pass |
1 2 3 4 5 6 7 8 9 10 11 12 | nd3 = np.random.randint( 1 , 20 ,size = ( 3 , 4 )) print (nd3) print ( '维度的个数' ,nd3.ndim) print ( '维度的大小' ,nd3.shape) print ( '数据类型' ,nd3.dtype) 展示: [[ 11 17 5 6 ] [ 17 1 12 2 ] [ 13 9 10 16 ]] 维度的个数 2 维度的大小 ( 3 , 4 ) 数据类型 int32 |
注意点:
1、如果没有指定最大值,只是指定了最小值,范围是[0,最小值)
2、如果有最小值,也有最大值,范围为[最小值,最大值)
1.2 序列创建
1.2.1 array
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 通过列表进行创建 nd4 = np.array([ 1 , 2 , 3 ]) 展示: [ 1 2 3 ] 通过列表嵌套列表创建 nd5 = np.array([[ 1 , 2 , 3 ],[ 4 , 5 ]]) 展示: [ list ([ 1 , 2 , 3 ]) list ([ 4 , 5 ])] 综合 nd4 = np.array([ 1 , 2 , 3 ]) print (nd4) print (nd4.ndim) print (nd4.shape) print (nd4.dtype) nd5 = np.array([[ 1 , 2 , 3 ],[ 4 , 5 , 6 ]]) print (nd5) print (nd5.ndim) print (nd5.shape) print (nd5.dtype) 展示: [ 1 2 3 ] 1 ( 3 ,) int32 [[ 1 2 3 ] [ 4 5 6 ]] 2 ( 2 , 3 ) int32 |
1.2.2 zeros
1 2 3 4 5 6 7 8 9 10 11 | nd6 = np.zeros(( 4 , 4 )) print (nd6) 展示: [[ 0. 0. 0. 0. ] [ 0. 0. 0. 0. ] [ 0. 0. 0. 0. ] [ 0. 0. 0. 0. ]] 注意点: 1 、创建的数里面的数据为 0 2 、默认的数据类型是 float 3 、可以指定其他的数据类型 |
1.2.3 ones
1 2 3 4 5 6 7 | nd7 = np.ones(( 4 , 4 )) print (nd7) 展示: [[ 1. 1. 1. 1. ] [ 1. 1. 1. 1. ] [ 1. 1. 1. 1. ] [ 1. 1. 1. 1. ]] |
1.2.4 arange
1 2 3 4 5 6 | nd8 = np.arange( 10 ) print (nd8) nd9 = np.arange( 1 , 10 ) print (nd9) nd10 = np.arange( 1 , 10 , 2 ) print (nd10) |
结果:
[0 1 2 3 4 5 6 7 8 9]
[1 2 3 4 5 6 7 8 9]
[1 3 5 7 9]
注意点:
- 1、只填写一位数,范围:[0,填写的数字)
- 2、填写两位,范围:[最低位,最高位)
- 3、填写三位,填写的是(最低位,最高位,步长)
- 4、创建的是一位数组
- 5、等同于np.array(range())
1.3 数组重新排列
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | nd11 = np.arange( 10 ) print (nd11) nd12 = nd11.reshape( 2 , 5 ) print (nd12) print (nd11) 展示: [ 0 1 2 3 4 5 6 7 8 9 ] [[ 0 1 2 3 4 ] [ 5 6 7 8 9 ]] [ 0 1 2 3 4 5 6 7 8 9 ] 注意点: 1 、有返回值,返回新的数组,原始数组不受影响 2 、进行维度大小的设置过程中,要注意数据的个数,注意元素的个数 nd13 = np.arange( 10 ) print (nd13) nd14 = np.random.shuffle(nd13) print (nd14) print (nd13) 展示: [ 0 1 2 3 4 5 6 7 8 9 ] None [ 8 2 6 7 9 3 5 1 0 4 ] 注意点: 1 、在原始数据集上做的操作 2 、将原始数组的元素进行重新排列,打乱顺序 3 、shuffle这个是没有返回值的 |
两个可以配合使用,先打乱,在重新排列
1.4 数据类型的转换
1 2 3 4 5 6 7 8 9 10 11 12 | nd15 = np.arange( 10 ,dtype = np.int64) print (nd15) nd16 = nd15.astype(np.float64) print (nd16) print (nd15) 展示: [ 0 1 2 3 4 5 6 7 8 9 ] [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. ] [ 0 1 2 3 4 5 6 7 8 9 ] 注意点: 1 、astype()不在原始数组做操作,有返回值,返回的是更改数据类型的新数组 2 、在创建新数组的过程中,有dtype参数进行指定 |
1.5 数组转列表
1 2 3 4 5 6 7 | arr1 = np.arange( 10 ) # 数组转列表 print ( list (arr1)) print (arr1.tolist()) 展示: [ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ] [ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ] |
numpy 多维数组相关问题
创建(多维)数组
1 | x = np.zeros(shape = [ 10 , 1000 , 1000 ], dtype = 'int' ) |
得到全零的多维数组。
数组赋值
1 | x[ * , * , * ] = * * * |
np数组保存
1 | np.save( "./**.npy" ,x) |
读取np数组
1 | x = np.load( "path" ) |
总结
以上为个人经验,希望能给大家一个参考,也希望大家多多支持IT俱乐部。